Power management strategy for sizing battery system for peak load limiting in a university campus
Bibliographic record
Abstract
This paper presents an effective approach to design the capacity of the battery energy storage system (BESS) when this latter is applied for peak load shaving in campus university buildings integrating roof-top photovoltaic (PV) modules. In our setting, electricity is mainly supplied from the utility grid to a pre-set power limit. However, once the load demand exceeds the pre-set power limit, photovoltaic modules and BESS can both be used to effectively limit the active power drawn from the utility grid. The sizing strategy aims to minimize investment on BESS and take advantages of the available PV modules to limit the campus peak load to a minimum billing demand. The main objective of the proposed method is to find the optimal size of the BESS that maximizes the annual benefits of the university campus when the BESS and PV modules are used for peak load shaving. A cost benefit analysis is implemented and which considers also factors influencing the BESS such as battery conversion losses. The approach is validated by case studies where the optimal battery system is sized for Quebec pricing scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".